Python Report Automation

A Python automation tool that processes raw sales datasets, cleans the data, generates summary reports, and creates visual business reports automatically.

The project demonstrates how Python can automate a typical business reporting workflow by converting raw transaction data into structured insights and visual reports.

The dataset used in this repository is a publicly available developer demo dataset.

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Features

• Supports multiple input formats
  • CSV\
  • TSV\
  • TXT\
  • JSON\
  • XLSX (optional with openpyxl)\
• Automatic column name detection\
• Cleans and standardizes sales-style datasets\
• Removes invalid or duplicate records\
• Generates cleaned CSV output\
• Generates text summary report\
• Generates Markdown summary report\
• Generates SVG charts\
• Generates HTML visual report\
• Optional PDF export support

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Workflow

Raw Data (input/)
        │
        ▼
automation.py
Data Cleaning + Summary Generation
        │
        ▼
Cleaned Data + Reports (output/)
        │
        ▼
report_visualizer.py
Charts + Visual Report

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Project Structure

python-report-automation/
│
├── input/
│   └── retail_sales_data - Sheet1.csv
│
├── output/
│   ├── cleaned_sales_data.csv
│   ├── sales_summary_report.txt
│   ├── sales_summary_report.md
│   ├── top_products_chart.svg
│   ├── category_sales_chart.svg
│   ├── city_sales_chart.svg
│   ├── monthly_sales_chart.svg
│   └── sales_visual_report.html
│
├── automation.py
├── report_visualizer.py
├── requirements.txt
├── README.md
└── .gitignore

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How It Works

1. automation.py

This script:

• scans the input/ folder
• detects supported input files
• maps flexible column names automatically
• cleans and standardizes the dataset
• removes invalid or duplicate rows

Outputs generated:

• cleaned_sales_data.csv
• sales_summary_report.txt
• sales_summary_report.md

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2. report_visualizer.py

This script reads the cleaned dataset and generates visual outputs.

It:

• calculates summary metrics
• generates business charts
• produces visual reports

Outputs generated:

• SVG charts
• HTML visual report
• optional PDF report

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Supported Input Types

Base version supports:

• .csv
• .tsv
• .txt
• .json

Optional support:

• .xlsx with openpyxl

Invalid or unsupported files return clear error messages instead of crashing.

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How to Run

Step 1 --- Run the automation script

python automation.py

This generates:

• cleaned dataset
• text summary report
• markdown summary report

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Step 2 --- Generate charts and visual report

python report_visualizer.py

This generates:

• charts
• HTML visual report
• optional PDF report

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Output Files

After running both scripts, the output/ folder will contain:

• cleaned_sales_data.csv
• sales_summary_report.txt
• sales_summary_report.md
• top_products_chart.svg
• category_sales_chart.svg
• city_sales_chart.svg
• monthly_sales_chart.svg
• sales_visual_report.html

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Example Use Case

Businesses often export raw sales data from internal systems.
This project demonstrates how Python automation can transform those raw files into:

• cleaned datasets
• summary reports
• visual dashboards

automatically.

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Skills Demonstrated

• Python scripting
• data cleaning automation
• flexible file parsing
• business reporting automation
• automated visualization
• error-safe workflow design

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Companion Project

If you want to build your own AI assistant that can help with tasks like:

• cleaning messy datasets
• generating business reports
• building automation scripts

you can explore my companion project:

.pyLee — Python Automation Engineer Assistant

This project demonstrates how an AI agent can assist in generating practical Python automation tools for real-world workflows.

🔗 Explore the project here:
https://github.com/sayeduzzaman-bracu/pyLee-python-automation-assistant

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Author

Sayed Uz Zaman
Python Automation & AI Agent Developer